The amount of data traffic on networks is rapidly increasing as services provided via networks keep evolving. For example, the increasing use of over-the-top video streaming services and on-line collaboration services has greatly increased traffic on both home and work networks. Certain network flows (e.g., interactive video, telepresence, virtual reality/augmented reality (VR/AR), and artificial intelligence (AI)) are more effective when they have low latency. Missed packet delivery deadlines may result in reduced user satisfaction with these services. Currently available QoS may not be able to meet these latency goals. Similarly, reserving bandwidth for these services may interfere with overall traffic on the network.
Currently it is difficult to realize network-wide low latency in a distributed network. Existing flow-control, rate control, and/or congestion control algorithms either have weak control on the overall latency or use complex protocols between hosts and routers. Reasons for the poor performance of existing algorithms include the amount of information used to guide router scheduling, and that routers may not be able to quickly and efficiently manipulate data in their queues.